1. Field of the Disclosure
The present disclosure relates to robotic control using spiking neuron networks.
2. Description of Related Art
Robotic control systems, such as the system 100 shown in FIG. 1, may comprise a controller apparatus 120 configured to control a plant 110. The plant 110 may include, for example, a robotic arm. The controller 120 may receive an input signal 102, and may generate a control signal 108. The plant 110 may provide feedback signal 106 to the adaptive controller 120.
Artificial neuron networks (ANN) may be used to implement controller 120 logic. Spiking neuron networks (SNN) may represent a special class of ANN, where neurons communicate by sequences of spikes. These networks may typically employ a pulse-coded mechanism, which may encode information using timing of the pulses. Such pulses (also referred to as “spikes” or ‘impulses’) may be short-lasting (typically on the order of 1-2 ms) discrete temporal events. Several exemplary embodiments of such encoding are described in a commonly owned and co-pending U.S. patent application Ser. No. 13/152,084 filed Jun. 2, 2011 and entitled “APPARATUS AND METHODS FOR PULSE-CODE INVARIANT OBJECT RECOGNITION”, and co-owned U.S. patent application Ser. No. 13/152,119 filed Jun. 2, 2011 and entitled “SENSORY INPUT PROCESSING APPARATUS AND METHODS”, issued as U.S. Pat. No. 8,942,466 on Jan. 27, 2015, each being incorporated herein by reference in its entirety.
As shown in FIG. 1, the controller may receive one or more physical inputs (102, 106) that may individually comprise continuous (e.g., analog) and/or discrete (e.g., digital) signals describing various variables. Examples of such various variables may include one or more of temperature, voltage, current, orientation, position, plant state, and other signals. The inputs 102, 106 may generally be referred to as sensory data and are encoded into spikes by an analog-to-spike converter, which inputs to the SNN of the controller 120. Different strategies may be used to encode sensory data, including, for example, modeling the transformation of sensory receptors in the central nervous system (e.g. the tactile mechanoreceptor of the skin), which may take a (physical) variable (e.g., skin deformation) and transform it into trains of spikes. Such sensory response models may be cumbersome to construct, particularly when converting inputs of varying dynamical range and/or different nature. Furthermore, sensor upgrade and/or replacement may necessitate changes of the sensor model and, hence, controller logic (e.g., software, firmware and or hardware).
The controller may receive a non-spiking reinforcement signal. The controller may be configured to generate a non-spiking (e.g., continuous analog and/or discrete digitized) control signal 108. In order to generate a control signal, the controller (e.g., the controller 120 of FIG. 1) may combine multiple encoded sensory streams.
Existing control systems may need to treat inputs differently depending on their origin, and may thus be able to treat only labeled inputs, e.g. an input is for velocity, another acceleration from particular labeled modalities (e.g. tactile, vision, auditory). If the inputs are inadvertently mixed, the system may not function properly (e.g. if the acceleration signal is connected to the controller velocity input, or the tactile signal is connected to the controller visual input). Another possibility is for the sensors signals to change due to sensor degradations, external conditions, or other reasons, the controller, expecting particular pre-defined signals may not be able to deal with such changes.